Computational chemistry tools, from quantum chemistry techniques to molecular modeling, have greatly contributed to a number of fields, ranging from geophysics and material chemistry to structural biology and drug design. Dangerous, expensive, and laborious experiments can be often replaced “in silico” by accurate calculations. In drug discovery, a number of techniques at various levels of accuracy and computational cost are in use. Methods on the more accurate end of the spectrum such as fully atomistic molecular simulations have been shown to be able to reliably predict a number of properties of interest, such as the binding pose or the binding free energy. However, they are computationally expensive. This fact has so far hampered the systematic application of simulation‐based methods in drug discovery, while inexpensive heuristic molecular modeling methods, such as protein–ligand docking are routinely used.

However, things are rapidly changing and the potential of atomistic biomolecular simulations in academic and industrial drug discovery is becoming increasingly clear. The question is whether we can expect an evolution or a revolution in this field. There are examples of other areas of life sciences where a revolution took or is taking place. For example, sequencing of the human genome took a decade and was funded by governments of several countries. Today, sequencing of eukaryotic genomes has become a routine, and a million‐genome project is on the way owing to highly efficient and inexpensive parallel sequencing technology. Similarly, genetic manipulations are becoming significantly easier and more efficient owing to CRISPR/Cas technology. At the same time, the deep learning revolution is having a deep impact on many fields. The open question is whether we can expect such a revolution in biomolecular simulations due to new groundbreaking technology and convergence with machine learning techniques or a stepwise evolution due to the availability of new hardware, of grid and cloud resources, as well as advances in force‐field accuracy, enhanced sampling techniques, and other achievements.

The aim of this book is to report on the current state and promising future directions for biomolecular simulations in drug discovery. Although we personally believe that there is true potential for a simulation‐based revolution in drug discovery, we will let the readers draw their own conclusions.

In the first part of the book, called Principles, we give an overview of biomolecular simulation techniques with focus on modeling protein–ligand interactions. When applying any molecular modeling method, we have to ask the question how accurate is the method in comparison with the experiment. There are three major factors influencing the overall accuracy of biomolecular simulations. First, the method itself is approximative. Second, we use a simplified structure–energy relationship (such as molecular mechanics force field), which is approximative, especially for new classes of molecules. And, finally third, the simulated system is an image of a single or few molecules observed for a short time in contrast to the experiment that typically provides observations averaged over a vast number of molecules and over a significantly longer time. In the other words, sampling of states in the simulation may be incomplete compared to sampling in the experiment. These issues are discussed in Chapter 1. Chapter 2 focuses on the “sampling problem,” in contexts relevant to drug discovery, namely, in modeling of protein–protein, protein–peptide, and protein–ligand interactions.

The second part of the book is called Advanced Algorithms. It presents algorithms used to solve problems presented in the first part of the book, especially the sampling problem. It is possible to artificially force the system to sample more states than in a conventional molecular simulation. The dynamics in such simulations is biased, but it is possible to derive statistically meaningful long‐timescale behavior and free energies from such simulations. These techniques, referred to as enhanced sampling techniques, are presented in Chapter 3. The methods include sampling enhancement obtained by raising the temperature (tempering methods), methods employing artificial potentials or forces acting on selected degrees of freedom, combined approaches, and other methods.

The traditional approach to evaluate protein–ligand interactions in drug discovery is based on thermodynamics, i.e. measurement or prediction of K _i , IC₅₀, binding ΔG, or similar parameters. However, recently it turned out that kinetics of protein–ligand binding and unbinding is highly important, often more important than the thermodynamics. Markov state models presented in Chapter 4 provide an elegant way to describe thermodynamics and kinetics of the studied process from various types of molecular simulations.

Other solutions to the sampling problem are based on a simplified representation of the studied system or of its dynamics. These approaches are covered in Chapters 5 and 6. Chapter 5 presents an alternative sampling approach based on a Monte Carlo method: PELE. The dynamics of the system is simplified to harmonic vibrations of a protein and translations and rotations of a ligand. This is used in each step to propose the new state of the system, which is either accepted or rejected in the spirit of the Monte Carlo method. The algorithm is highly efficient in exploring ligand and target dynamics, as demonstrated by a number of ligand design applications. Chapter 6 presents an overview of network models. It is possible to represent the structure of a protein as a network of interactions. This approach makes it possible to simplify (coarse grain) the studied system, study the system in terms of normal modes, and combine these coarse‐grained models with fine‐grained models.

The third part of the book is called Applications and Success Stories. Chapter 7 provides an overview of the applications of molecular modeling methods in drug discovery. It presents various molecular modeling methods, including quantitative structure–activity relationship (QSAR) and ligand‐based models, pharmacophore modeling, protein–ligand docking, biomolecular simulations, and quantum chemistry methods. Each technique is presented together with its practical impact in drug development and with examples of approved drugs.

Chapters 8 and 9 focus on the largest group of drug targets – G protein–coupled receptors (GPCRs), one from the academic and one from industrial perspective. The issues covered by these chapters include sampling problem, the role of membrane and water, free energy predictions, ligand binding kinetics, and others. Simulation of GPCRs is challenging partially due to their membrane environment. Another important group of membrane‐bound targets are ion channels covered in Chapter 10. Special topics related to ion channels, such as modeling of ion selectivity and ion conductance, are described in this chapter.

Allostery is a very important topic when studying protein–ligand interactions because many ligands bind to sites other than those expected and/or make an effect on sites other than the binding one. Allostery, its thermodynamics, ways of modeling, and application on various drug targets are described in Chapter 11.

The last two chapters are focused on specific topics of current relevance in drug discovery. Chapter 12 presents the way to address protein misfolding and aggregation by biomolecular simulations. This is illustrated on polyglutamine and polyasparagine protofibrils from simulations to thermodynamic models of aggregate formation. Chapter 13 targets the cell cycle and the role of ubiquitin‐mediated proteolysis. In the example of Cdc34, it is illustrated how biomolecular simulations can be integrated with structural biology and other methods to elucidate the structure and dynamics of a drug target.

This book was realized thanks to the invitation from Prof. Gerd Folkers and thanks to support by him and other series editors. We gratefully acknowledge their support and patience. We also thank Dr. Frank Weinreich, Dr. Stefanie Volk, and Dr. Sujisha Karunakaran from Wiley‐VCH for their support and pleasant collaboration on this volume.

We believe that the book can add more dynamics to drug design and more drug design to biomolecular simulations.